SoroushVahidi/scidocs huggingface dataset:
Building a case-based diet recommendation system without a knowledge engineer
(2003)

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.
A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling
(2010)

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.
Single-stage posterior corpectomy and expandable cage placement for treatment of thoracic or lumbar burst fractures.
(2009)

STUDY DESIGN
A prospective study was performed.


OBJECTIVE
To assess an unusual technique for corpectomy and expandable cage placement via single-stage posterior approach in acute thoracic or lumbar burst fractures.


SUMMARY AND BACKGROUND DATA
Burst fractures represent 10% to 20% of all spine injuries at or near the thoracolumbar junction, and can cause neurologic complications and kyphotic deformity. The goal of surgical intervention is to decompress the neural elements, restore vertebral body height, correct angular deformity, and stabilize the columns of the spine.


METHODS
The study comprised 14 patients (8 women and 6 men aged 40.3 years) who had 1 spinal burst fracture between T8 and L4 and who underwent single-stage posterior corpectomy, circumferential reconstruction with expandable-cage placement, and transpedicle screwing between January 2003 and May 2005. Neurologic status was classified using the American Spinal Injury Association (ASIA) impairment scale and functional outcomes were analyzed using a visual analogue scale (VAS) for pain. The kyphotic angle (alpha) and lordotic angle (beta) were measured in the thoracic or thoracolumbar and lumbar regions, respectively. RESULTS.: The mean follow-up time was 24 months (range, 12-48 months). Neurologic status was in 7 patients (preop: ASIA-E, postop: unchanged), 2 patients (preop: ASIA-D, postop: 1 unchanged, 1 improved to ASIA-E), 3 patients (preop: ASIA-C, postop: 2 improved to ASIA-D, 1 improved to ASIA-E), 2 patients (preop: ASIA-B, postop: 1 improved to ASIA-C, 1 unchanged). The mean operative time was 187.8 minutes. The mean blood loss was 596.4 mL. Regarding postoperative complications, 1 patient experienced transient worsening of neurologic deficits and 1 patient developed pseudarthrosis. The mean preoperative VAS score was 8.21 and the mean postoperative VAS score was 2.66 (P < 0.05). The mean preoperative kyphotic angle for the 11 individuals with the thoracic or thoracolumbar burst fractures was 24.6 degrees and the mean preoperative lordotic angle for the 3 individuals with lumbar burst fractures was 10.6 degrees. The corresponding values at 12 months postsurgery were 17.1 degrees and 13.6 degrees.


CONCLUSION
This single-stage posterior approach for acute thoracic and lumbar burst fractures offers some advantages over the classic combined anterior-posterior approach. The results from this small series suggest that a single-stage posterior approach should be considered in select cases.
A holistic approach to service survivability
(2003)

We present SABER (Survivability Architecture: Block, Evade, React), a proposed survivability architecture that blocks, evades and reacts to a variety of attacks by using several security and survivability mechanisms in an automated and coordinated fashion. Contrary to the ad hoc manner in which contemporary survivable systems are built-using isolated, independent security mechanisms such as firewalls, intrusion detection systems and software sandboxes-SABER integrates several different technologies in an attempt to provide a unified framework for responding to the wide range of attacks malicious insiders and outsiders can launch.
 This coordinated multi-layer approach will be capable of defending against attacks targeted at various levels of the network stack, such as congestion-based DoS attacks, software-based DoS or code-injection attacks, and others. Our fundamental insight is that while multiple lines of defense are useful, most conventional, uncoordinated approaches fail to exploit the full range of available responses to incidents. By coordinating the response, the ability to survive successful security breaches increases substantially.
 We discuss the key components of SABER, how they will be integrated together, and how we can leverage on the promising results of the individual components to improve survivability in a variety of coordinated attack scenarios. SABER is currently in the prototyping stages, with several interesting open research topics.
Hourly rounding to improve nursing responsiveness: a systematic review.
(2014)

UNLABELLED
The aims of this study were to synthesize the evidence concerning the effect of hourly rounding programs on patient satisfaction with nursing care and discuss implications for nurse administrators.


BACKGROUND
Patient satisfaction is a key metric that influences both hospital ratings and reimbursement. Studies have suggested that purposeful nursing rounds can improve patient satisfaction, but the evidence to date has not been systematically examined.


METHODS
A systematic review of published literature and GRADE analysis of evidence regarding nursing rounds were conducted.


RESULTS
There is little consistency in how results of hourly rounds were measured, precluding quantitative analysis. There is moderate-strength evidence that hourly rounding programs improve patients' perception of nursing responsiveness. There is also moderate-strength evidence that these programs reduce patient falls and call light use.


CONCLUSIONS
Nurse administrators should consider implementing an hourly rounding program while controlled trials discern the most cost-effective approach.
Extreme gigantomastia in pregnancy: case report—my experience with two cases in last 5 years
(2010)

We present an extreme case of gigantomastia in pregnancy during the second gemelar pregnancy of a 30-year-old woman. Her first pregnancy was 8 years ago, was also gemelar and she delivered with caesarean section. From the beginning of her current pregnancy, the patient noted steady growth of both of her breasts that reached enormous dimensions at the end of the pregnancy. This kind of breast changes did not occur during her first pregnancy. The patient also suffered from myasthenia gravis that was in remission during this pregnancy, without any therapy. The patient was in the 38 weeks of gestation, and a delivery with caesarean section was performed in line with the reduction of her breasts. The main reasons that led me to perform these two interventions as one act were the fact that puerperal mastitis could develop on these enormous breasts, further the small regression of these huge breasts during the bromocriptine treatment, as well as the intention to avoid other operative traumas, considering possibility of exacerbation of myasthenia gravis. I had already performed bilateral reduction mammaplasty with free areola-nipple graft, when a tissue with total weight of 20 kg (2 × 10 kg) was removed. The patient had an excellent post-operation recovery course.
Classification of copy move forgery and normal images by ORB features and SVM classifier
(2017)

Today, the characterization of the technological age is done by the digital images spread. They are the most common form of conveying information whether through internet, newspapers, magazines, or scientific journals. They are used as a strong proof of various crimes and as evidence used for various purposes. The modification, capturing or creating of the image has become easier and available with the emergence of means of image editing and processing tools. One of the most important and popular types of image forgery is a copy-move forgery in which an image part is copied and then pasted into the same image that has the intention of hiding something important or showing a false scene. Because the important properties of the copied parts come from the same image, such as brightness, noise, and texture which will be compatible with the entire image that makes more difficult for experts for the detection and distinguishing the alteration. Usually, the detecting copy move forgery conventional techniques suffer severely from the time-consuming problem. The evaluation of the improved method had been done using (150) images that were selected from two different datasets, “CoMoFoD” and “MICC-F2000”. Experimental results show that the improved method can accurately and quickly reveal the doubled regions of a tampered image. In addition, greatly reducing the processing time in comparison to the Khan algorithm, and the accuracy is kept at the same level. Owing to the availability and technological advancement of the image editing sophisticated tools, there is an increase in the loss of authentication in digital images. Thus, this led us to the proposal of different detection techniques that checks whether the digital images are forged or authentic. The specific type of forgery technique is copy move forgery in which widely used research topic is detection under digital image forensics. In this thesis, an enhancement of copy move image forgery classification is done by implementing hybrid features with classification algorithms like SIFT with SVM and EM algorithm and ORB with SVM and EM.The technique works by applying Firstly the DCT on an image and then on a resultant image, SIFT is obtained after applying DCT. A supervised learning method is proposed for classifying a copy-move image forgery of TIFF, JPEG, and BMP. The process starts with reducing the color of the photos. Achieve the accuracy more than 90%.
Strength-based interventions : Their importance in application to the gifted
(2017)

Positive psychology has revived psychology’s abandoned interest in the study of morally positively valued traits (the so-called character strengths) and virtues. We review literature generated on strength-based approaches and focus on applications in the so-called positive psychology interventions. There seems to be great potential in this approach for research in the field of giftedness and, of course, also when practically working with gifted children and adolescents. We highlight some ideas for future research directions. DOI: https://doi.org/10.1177/0261429416640334 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-128345 Accepted Version Originally published at: Proyer, René T; Gander, Fabian; Tandler, Nancy (2017). Strength-based interventions: Their importance in application to the gifted. Gifted Education International, 33(2):118-130. DOI: https://doi.org/10.1177/0261429416640334 Strength-based interventions: Their importance in application to the gifted René T. Proyer Martin-Luther University Halle-Wittenberg, Germany University of Zurich, Switzerland Fabian Gander University of Zurich, Switzerland Nancy Tandler Martin-Luther University Halle-Wittenberg, Germany René T. Proyer is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) and the Department of Psychology at the University of Zurich (Switzerland). Fabian Gander is at Department of Psychology at the University of Zurich (Switzerland). Nany Tandler is at the Department of Psychology at the Martin-Luther University Halle-Wittenberg (Germany) The preparation of this paper has been facilitated by research grants of the Swiss National Science Foundation (SNSF; 100014_132512 and 100014_149772) awarded to RTP. Correspondence concerning this article should be addressed to René Proyer, MartinLuther University Halle-Wittenberg (Germany), Emil-Abderhalden-Straße 26-27, 06108 Halle (Saale), Germany; E-mail: rene.proyer@psych.uni-halle.de
Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine
(2010)

Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
(2000)

ÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. Index TermsÐHandwriting recognition, on-line, off-line, written language, signature verification, cursive script, handwriting learning tools, writer authentification.
Automated Human Behavior Prediction through Handwriting Analysis
(2010)

Handwriting Analysis or Graphology is a scientific method of identifying, evaluating and understanding personality through the strokes and patterns revealed by handwriting. Handwriting reveals the true personality including emotional outlay, fears, honesty, defenses and many others. Professional handwriting examiners called graphologist often identify the writer with a piece of handwriting. Accuracy of handwriting analysis depends on how skilled the analyst is. Although human intervention in handwriting analysis has been effective, it is costly and prone to fatigue. Hence the proposed methodology focuses on developing a tool for behavioral analysis which can predict the personality traits automatically with the aid of a computer without the human intervention. In this paper a method has been proposed to predict the personality of a person from the baseline, the pen pressure, the letter‘t’, the lower loop of letter ‘y’ and the slant of the writing as found in an individual’s handwriting. These parameters are the inputs to a Rule-Base which outputs the personality trait of the writer.
Human Action Recognition under Log-Euclidean Riemannian Metric
(2009)

This paper presents a new action recognition approach based on local spatio-temporal features. The main contributions of our approach are twofold. First, a new local spatio-temporal feature is proposed to represent the cuboids detected in video sequences. Specifically, the descriptor utilizes the covariance matrix to capture the self-correlation information of the low-level features within each cuboid. Since covariance matrices do not lie on Euclidean space, the Log-Euclidean Riemannian metric is used for distance measure between covariance matrices. Second, the Earth Mover’s Distance (EMD) is used for matching any pair of video sequences. In contrast to the widely used Euclidean distance, EMD achieves more robust performances in matching histograms/distributions with different sizes. Experimental results on two datasets demonstrate the effectiveness of the proposed approach.
How Personal Experience and Technical Knowledge Affect Using Conversational Agents
(2018)

Conversational agents (CA) use dialogues to interact with users so as to offer an experience of naturalistic interaction. However, due to the low transparency and poor explanability of mechanism inside CA, individual's understanding of CA's capabilities may affect how the individual interacts with CA and the sustainability of CA use. To examine how users' understanding affect perceptions and experiences of using CA, we conducted a laboratory study asking 41 participants performed a set of tasks using Apple Siri. We independently manipulated two factors: (1) personal experience of using CA, and (2) technical knowledge about CA's system model. We conducted mixed-method analyses of post-task usability measures and interviews, and confirmed that use experience and technical knowledge affects perceived usability and mental models differently.
Dynamic TXOP HCCA reclaiming scheduler with transmission time estimation for IEEE 802.11e real-time networks
(2012)

IEEE 802.11e HCCA reference scheduler guarantees Quality of Service only for Constant Bit Rate traffic streams, whereas its assignment of scheduling parameters (transmission time TXOP and polling period) is too rigid to serve Variable Bit Rate (VBR) traffic.
 This paper presents a new scheduling algorithm, Dynamic TXOP HCCA (DTH). Its scheduling scheme, integrated with the centralized scheduler, uses both a statistical estimation of needed transmission duration and a bandwidth reclaiming mechanism with the aim of improving the resource management and providing an instantaneous dynamic Transmission Opportunity (TXOP), tailored to multimedia applications with variable bit rate. Performance evaluation through simulation, confirmed by the scheduling analysis, shows that DTH is suitable to reduce the transmission queues length. This positively impacts on the delay and on packets drop rate experienced by VBR traffic streams.
Netradar - Measuring the wireless world
(2013)

There exists a number of network measurement tools and web-based services for end users. Almost all of them focus on network bandwidth and possibly latency, and simply report the results to the user. This paper presents Netradar, a somewhat different measurement service. We analyze network bandwidth and latency but also over ten other parameters. We dwell into the data and show various coverage maps and statistics on our web site for the benefit of the end users. Our long term goal is to be able to answer the simple question: why did I get the indicated result at a given time and place?
A Behavioral Model of Rational Choice
(2007)

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Building Domain-Specific Search Engines with Machine Learning Techniques
(1999)

Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over .summer camps. Unfortunately these domain-specific search engines are difficult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contaius over 50,000 papers and is publicly available at ~w. cora.justres earch, com.